Senior Data Engineer

The Colorado Rockies Baseball Club is building an integrated baseball information platform to support decision-making. The Senior Data Engineer will design and maintain data pipelines and infrastructure, ensuring data accuracy and reliability while providing technical leadership and mentoring to other engineers.

Responsibilities:

• Design, build, and maintain scalable ETL and ELT pipelines that ingest data from external vendors, internal systems, and baseball data sources
• Develop and maintain cloud-based data infrastructure supporting storage, transformation, orchestration, analytics, and internal applications
• Model, structure, and optimize data for use by analysts, developers, researchers, and other Baseball Operations users
• Monitor and troubleshoot production pipelines, identify root causes, resolve data quality issues, and improve reliability and performance
• Partner with developers, analysts, and Baseball Operations stakeholders to translate business needs into scalable data solutions
• Establish and promote engineering best practices related to code quality, documentation, testing, version control, observability, and maintainability
• Evaluate, integrate, and manage new baseball data sources, vendor feeds, APIs, and file-based deliveries
• Support data infrastructure used for analytics, machine learning, artificial intelligence, and internal decision-support tools
• Mentor other engineers through technical guidance, code reviews, project support, and skill development

Requirements:

• Five or more years of data engineering experience, or an equivalent combination of experience and demonstrated capability
• Significant experience building and operating reliable production data pipelines (ETL/ELT)
• Advanced SQL and strong proficiency in Python
• Hands-on experience with a major cloud platform (AWS, Azure, or GCP) and a modern data platform (Databricks, Snowflake, or equivalent)
• Experience developing distributed data workloads with Apache Spark / PySpark
• Experience across data modeling, orchestration, testing, observability, and production troubleshooting
• Ability to guide technical decisions and mentor less experienced engineers
• Strong communication and collaboration with both technical and non-technical stakeholders
• Experience with baseball data and Baseball Operations workflows — tracking, scouting, performance, player development, sports science, or biomechanics
• Experience with lakehouse, data warehouse, or data lake architecture, including scalable ingestion patterns and dimensional or layered data modeling
• Experience with workflow orchestration (Apache Airflow, Dagster, Prefect, or similar) and pipeline monitoring, data-quality testing, and operational support
• Experience building data pipelines, feature datasets, or platform infrastructure for machine learning and artificial intelligence workloads
• Familiarity with infrastructure as code (Terraform, AWS CDK, or CloudFormation)
• Bachelor's degree in a related technical field, completion of an immersive technical program, or equivalent practical experience
• Familiarity with AI-assisted development tools and their responsible use in modern engineering workflows